We evaluate our proposed method on predator-prey and StarCraft II Multi-agent Challenge, which have recently become popular benchmarks in multi-agent reinforcement learning
under partially observable settings [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. We compare the performance of LFF with a variety of state-of-the-art communication algorithms,
including CommNet [7]}, TarMAC [8]}, NDQ [9]}, GA-Comm [10]}, and IS [11]}.
For fairness, we combine our baselines with VDNS since most of them do not deal with credit assignment problems (except for NDQ).
We also conduct ablation studies to answer the following questions: (i) How does our method differ from intention sharing based on pre-defined
topologies or existing graph methods? (ii) How do the number of groups \(k\)  and the depth boundary \(d\)  influence the communication performance and cost time?
All results are averaged over five seeds, and the standard error of means are shaded. More details about the implementation of baselines and our method are included in Appendix.
